1. Technical Field
The present invention is directed generally toward utility service delivery and the use of distributed intelligence and networking in the optimization of utility, especially electrical, service delivery. Applications in this field are popularly characterized as “Smart Grid” applications.
2. Background
The electrical grid in the United States and most other areas of the world is historically divided into two networks: the transmission grid, and the distribution grid. The transmission grid originates at a generation point, such as a coal-burning or atomic power plant, or a hydroelectric generator at a dam. DC power is generated, converted to high-voltage AC, and transmitted to distribution points, called distribution substations, via a highly controlled and regulated, redundant, and thoroughly instrumented high-voltage network which has at its edge a collection of distribution substations. Over the last century, as the use of electrical power became more ubiquitous and more essential, and as a complex market in the trading and sharing of electrical power emerged, the technology of the transmission grid largely kept pace with the technological requirements of the market.
The second network, the distribution grid, is the portion of the electrical grid which originates at the distribution substations and has at its edge a collection of residential, commercial, and industrial consumers of energy. In contrast to the transmission grid, the technology of the distribution grid has remained relatively static since the mid-1930s until very recent years. Today, as concern grows over the environmental effects of fossil fuel usage and the depletion of non-renewable energy sources, electrical distribution technology is increasingly focused on optimization of the distribution grid. The goals of this optimization are energy conservation, resource conservation, cost containment, and continuity of service.
To optimize electrical service delivery, the operators of the network must be able to quantify and anticipate the demand for power that the distribution grid is expected to provide. To achieve the goals of conservation, cost containment, and continuity of service, it is also necessary to be able to manage and sometimes curtail that demand.
Historically, utilities acquired information about household and commercial usage only when meters were read. Thus, load profiles were based on historical data year to year and on trend analysis as the characteristics of the loads changed. Because of this paucity of information, the utilities have been forced to over-deliver service, so that, for example, a standard outlet or socket in a consumer residence might deliver 122V AC when the loading devices used there are designed and rated to operate at as low as 110 V AC. This disparity provides a substantial opportunity for conservation, but the opportunity cannot be realized without better information about the pattern of demand.
The earliest attempts at conservation voltage reduction were made at the substation level, using instrumentation at the substation and a load-tap changer on the substation transformer. This coarse-grained method is effective for keeping voltages at the load points within specifications, but, to keep some end points from being under-served, requires a safety margin to be employed that results in most end points being slightly over-served, as described above. Finer-grained information is necessary to achieve substantial improvements in conservation.
One well-known experiment in the prior art of conservation voltage reduction involved attaching individual voltage regulators to private residences at the metered point. This model provides significant immediate benefits to individual residential accounts, but utilities must wait for historical data to realize gains such as reduced use of “peaker” plants and avoiding purchasing energy on the spot market. Utilities require finer-grained load pattern data in near-real-time to achieve such gains during the first year of operation of a CVR program.
One potential source of such fine-grained data is communicating “smart meters” which can report voltages. This approach has been piloted and yielded reductions in power usage up to 3%. Because the effective bandwidth per meter of the typical radio-based AMI mesh network does not permit every meter to report its voltage fluctuations frequently in near-real-time throughout the day, these solutions sample only a limited selection of load points in real-time. The load projections and data thus obtained can be used to drive demand management applications because the smart meters are capable of two-way communications.
Another approach to the conservation problem has been the use of in-facility displays of real-time energy usage, engaging the consumer in the activity of reducing demand. While these techniques are effective for commercial and industrial consumers with automated facility management systems, efforts to engage residential consumers in actively managing their own consumption have met with limited success. Residential systems for energy management are an application of Home Area Networking (HAN).